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Add citation and grant acknowledgement to readme (#1)

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- Add citation and grant acknowledgement to readme (64fc094d13a32c2633e0869a63bb79babee9f003)
- Add keywords to readme (8c3583f7e078d9732fe25563ad4835df4e414e5b)


Co-authored-by: Elizabeth Campolongo <egrace479@users.noreply.huggingface.co>

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  1. README.md +47 -1
README.md CHANGED
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  ---
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  license: mit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  ## Model Card for butterfly_segmentation_yolo_v8
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  ## How to Get Started with the Model
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- To view applications of how to load in the model file and predict masks on images, please refer to [this github repository](https://github.com/Imageomics/wing-segmentation)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: mit
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+ language:
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+ - en
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+ tags:
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+ - biology
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+ - CV
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+ - images
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+ - animals
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+ - lepidoptera
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+ - butterflies
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+ - segmentation
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+ - heliconius
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+ - forewings
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+ - hindwings
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+ - separated wings
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+ - full body
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+ - butterfly
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+ - RGB
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+ - ruler
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+ - whitebalance
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+ - label
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+ - colorchecker
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  ---
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  ## Model Card for butterfly_segmentation_yolo_v8
 
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  ## How to Get Started with the Model
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+ To view applications of how to load in the model file and predict masks on images, please refer to [this github repository](https://github.com/Imageomics/wing-segmentation)
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+ ## Citation
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+ **BibTeX:**
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+
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+ ```
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+ @software{Ramirez_Lepidoptera_Wing_Segmentation_2024,
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+ author = {Ramirez, Michelle},
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+ doi = {10.5281/zenodo.10869579},
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+ month = mar,
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+ title = {{Lepidoptera Wing Segmentation}},
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+ url = {https://github.com/Imageomics/wing-segmentation},
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+ version = {1.0.0},
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+ year = {2024}
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+ }
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+ ```
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+ **APA:**
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+ Ramirez, M. (2024). Lepidoptera Wing Segmentation (Version 1.0.0) [Computer software]. https://doi.org/10.5281/zenodo.10869579
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+ ## Acknowledgements
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+ The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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